Inferring tumour purity and stromal and immune cell admixture from expression data

Kosuke Yoshihara, Maria Shahmoradgoli, Emmanuel Martínez, Rahulsimham Vegesna, Hoon Kim, Wandaliz Torres-Garcia, Victor Treviño, Hui Shen, Peter W. Laird, Douglas A. Levine, Scott L. Carter, Gad Getz, Katherine Stemke-Hale, Gordon B. Mills and Roel G.W. Verhaak ()
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Kosuke Yoshihara: The University of Texas MD Anderson Cancer Centre
Maria Shahmoradgoli: The University of Texas MD Anderson Cancer Centre
Emmanuel Martínez: The University of Texas MD Anderson Cancer Centre
Rahulsimham Vegesna: The University of Texas MD Anderson Cancer Centre
Hoon Kim: The University of Texas MD Anderson Cancer Centre
Wandaliz Torres-Garcia: The University of Texas MD Anderson Cancer Centre
Victor Treviño: Catedra de Bioinformatica, Tecnologico de Monterrey, Campus Monterrey
Hui Shen: USC Epigenome Centre, University of Southern California
Peter W. Laird: USC Epigenome Centre, University of Southern California
Douglas A. Levine: Gynecology Service, Memorial Sloan-Kettering Cancer Centre
Scott L. Carter: The Broad Institute of Harvard and MIT
Gad Getz: The Broad Institute of Harvard and MIT
Katherine Stemke-Hale: The University of Texas MD Anderson Cancer Centre
Gordon B. Mills: The University of Texas MD Anderson Cancer Centre
Roel G.W. Verhaak: The University of Texas MD Anderson Cancer Centre

Nature Communications, 2013, vol. 4, issue 1, 1-11

Abstract: Abstract Infiltrating stromal and immune cells form the major fraction of normal cells in tumour tissue and not only perturb the tumour signal in molecular studies but also have an important role in cancer biology. Here we describe ‘Estimation of STromal and Immune cells in MAlignant Tumours using Expression data’ (ESTIMATE)—a method that uses gene expression signatures to infer the fraction of stromal and immune cells in tumour samples. ESTIMATE scores correlate with DNA copy number-based tumour purity across samples from 11 different tumour types, profiled on Agilent, Affymetrix platforms or based on RNA sequencing and available through The Cancer Genome Atlas. The prediction accuracy is further corroborated using 3,809 transcriptional profiles available elsewhere in the public domain. The ESTIMATE method allows consideration of tumour-associated normal cells in genomic and transcriptomic studies. An R-library is available on https://sourceforge.net/projects/estimateproject/ .

Date: 2013
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DOI: 10.1038/ncomms3612

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